A Practical Approach for Coordination of Plugged- In Electric Vehicles To Improve Performance and Power Quality of Smart Grid

This PhD research is undertaken by supplications including 14 peer-reviewed published articles over seven years research at Curtin University. This study focuses on a real-time Plugged-in Electric Vehicle charging coordination with the inclusion of Electric Vehicle battery charger harmonics in Smart Grid and future Microgrids with incorporation of Renewable Energy Resources. This strategy addresses utilities concerns of grid power quality and performance with the application of SSCs dispatching, active power filters or wavelet energy

Optimal dispatch of shunt capacitors and load tap changers in distorted distribution systems using ant colony algorithms

This thesis investigates the performances of a class of intelligent system algorithms in solving the volt/VAr/THD control problem for large distribution systems. For this purpose, optimal dispatch of Load Tap Changers (LTCs) and Switched Shunt Capacitors in distribution networks with high penetration of nonlinear loads is studied. The optimization problem consists of determination of LTC positions, switched shunt capacitors statuses and proper coordination of these switched elements such that power loss is minimized, voltage profile is improved and total harmonic voltage distortion (THDv) is acceptable while network and operational constraints are satisfied. The Decoupled Harmonic Power Flow (DHPF) is employed for solving the optimization problem. In the next step, an Ant Colony algorithm (ACA) is developed and implemented as an effective and new technique to capture the near global solution of the dispatch problem. Simulation results based on ACA, Genetic Algorithm (GA) and Fuzzy-GA are presented and compared to show the accuracy of the proposed approach.Finally, the application of the developed dispatch ACA in smart grids with Plug-In Electric Vehicle (PEV) charging activities in the residential networks is considered. ACA is first applied on the distribution part of the smart grid to minimize losses, improve voltage profile and mitigate harmonic distortions. Then, a smart load management (SLM) algorithm is proposed and tested for the coordination of PEVs on the residential feeders. The developed algorithm is tested on smart grid configuration with 449 buses consisting of the IEEE 31-bus distribution system connected to a number of low voltage residential feeders populated with PEVs. Simulation results are presented and compared for uncoordinated (random) and SLM coordinated PEV charging considering consumer designated priorities and charging zones

LTC and Switched Shunt Capacitor Scheduling in Smart Grid with Electric Vehicles and Wind Distributed Generation Systems

Future smart grids (SGs) are expected to include distributed generations (DG), plug-in electric vehicles (PEVs) and smart appliances, as well as nonlinear industrial loads that may decrease grid efficiency and deteriorate the quality of electric power. This paper performs optimal (load tap changer)LTC and switched shunt capacitor (SSC) in SGs with nonlinear loads, wind distributed generation (WDGs) systems and PEV charging at consumers’ premises and PEV charging stations (PEV-CSs). The substantial grid energy requirements at high PEV penetrations is assumed to be partially supplied by WDGs located within the distribution network. PEV charging is performed based on a recently proposed online maximum sensitivities selection based coordination algorithm (OL-MSSCA), nonlinear loads are assumed to inject low order odd current harmonics and WDGs are treated as negative PQ loads in the employed decoupled harmonic load flow (DHLF) algorithm. Simulations are performed for the modified IEEE 23kV distribution system with three WDGs, three PEV-CSs and 22 low voltage residential networks with PEVs. Impacts of PEV coordination and WDG on the LTC/SSC scheduling outcomes including grid losses, voltage profiles and THDs are investigated

Performance of heuristic optimization in coordination of plug-in electric vehicles charging

A heuristic load management (H-LMA) algorithm is presented for coordination of Plug-in Electric Vehicles (PEVs) in distribution networks to minimize system losses and regulate bus voltages. The impacts of optimization period T (varied from 15 minutes to 24 hours) and optimization time interval (varied 15 minutes to one hour) on the performance, accuracy and speed of the H-LMA is investigated through detailed simulations considering enormous scenarios. PEV coordination is performed by considering substation transformer loading while taking PEV owner priorities into consideration. Starting with the highest priority consumers, HLMA will use time intervals to distribute PEV charging within three designated high, medium and low priority time zones to minimize total system losses over period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results generated in MATLAB are presented for a 449 node distribution network populated with PEVs in residential feeders

A heuristic approach for coordination of plug-in electric vehicles charging in smart grid

In this paper, a heuristic load management algorithm (H-LMA) is proposed for Plug-in Electric Vehicles (PEVs) charging coordination. The proposed approach is aimed to minimize system losses over a period T (e.g., 24 hours) through re-optimizing the system at time intervals (e.g., 15 minutes) while regulating bus voltages through future smart grid communication system by exchanging signals with individual PEV chargers. Scheduling is performed based on the allowable substation transformer loading level and taking into account PEV owner preference/priority within three designated charging time zones. Starting with the highest priority consumers, H-LMA will distribute charging of PEVs within the selected priority time zones to minimize total system losses over a period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results are presented for different charging scenarios and are compared to demonstrate the performance of H-LMA for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs. The main contribution of this paper lies in the detailed simulations / analyses of the smart grid under study and highlighting the impacts of and T values on the performance of the proposed coordination approach in terms of accuracy and coordination execution time

Fuzzy Approach for Online Coordination of Plug-In Electric Vehicle Charging in Smart Grid

This paper proposes an online fuzzy coordination algorithm (OL-FCA) for charging plug-in electric vehicles (PEVs) in smart grid networks that will reduce the total cost of energy generation and the associated grid losses while maintaining network operation criteria such as maximum demand and node voltage profiles within their permissible limits. A recently implemented PEV coordination algorithm based on maximum sensitivity selection (MSS) optimization is improved using fuzzy reasoning. The proposed OL-FCA considers random plug-in of vehicles, time-varying market energy prices, and PEV owner preferred charging time zones based on priority selection. Impacts of uncoordinated, MSS, and fuzzy coordinated charging on total cost, gird losses, and voltage profiles are investigated by simulating different PEV penetration levels on a 449-node network with three wind distributed generation (WDG) systems. The main advantage of OL-FCA compared with the MSS PEV coordination is the reduction in the total cost it introduces within the 24h

Non-intrusive load monitoring and supplementary techniques for home energy management

The emerging smart grid technologies and rapid installations of smart meters is encouraging many consumers to implement home energy management systems (HEMSs) in order to decrease their electric utility bills and increase the efficiency of energy consumption. Intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM) are two approaches in the literature for appliance load monitoring (ALM) that make it possible for HEMSs to optimize energy utilization. However, most researchers have addressed NILM as the more practical option. In this paper, three basic methods for NILM are presented and supplementary techniques for improving the accuracy of NILM are discussed and compared. In addition, future research directions and challenges are highlighted

Optimal dispatch of LTC and switched shunt capacitors in smart grid with plug-in electric vehicles

Smart grids will facilitate transformation from the centralized, producer-controlled network to a less centralized and more consumer-interactive system while promoting distributed renewable energy resources, plug-in electric vehicles (PEVs) and smart appliances. It is well-known that random (uncoordinated) charging of PEV batteries can have detrimental impacts on voltage profiles and grid losses particularly during the peak load hours. This paper investigates the possibility of preventing or mitigating these issues by considering PEV charging activities in the optimal dispatch of LTC and switched shunt capacitors (SSCs). To do this, forecasted daily load curves of residential feeders with PEV charging are incorporated in the optimal LTC and SSC scheduling. Simulation results without and with optimal dispatch are presented for (un)coordinated charging of PEVs considering harmonic distortions due to the presence of nonlinear loads

An Insight into Practical Solutions for Electric Vehicle Charging in Smart Grid

The electrification of transportation has been developed to support energy efficiency and CO2 reduction. As a result, electric vehicles (EVs) have become more popular in the current transport system to create more efficient energy. In recent years, this increase in EVs as well as renewable energy resources (RERs) has led to a major issue for power system networks. This paper studies electrical vehicles (EVs) and their applications in the smart grid and provides practical solutions for EV charging strategies in a smart power system to overcome the issues associated with large-scale EV penetrations. The research first reviews the EV battery infrastructure and charging strategies and introduces the main impacts of uncontrolled charging on the power grid. Then, it provides a practical overview of the existing and future solutions to manage the large-scale integration of EVs into the network. The simulation results for two controlled strategies of maximum sensitivity selection (MSS) and genetic algorithm (GA) optimization are presented and reviewed. A comparative analysis was performed to prove the application and validity of the solution approaches. This also helps researchers with the application of the optimization approaches on EV charging strategies. These two algorithms were implemented on a modified IEEE 23 kV medium voltage distribution system with switched shunt capacitors (SSCs) and a low voltage residential network, including EVs and nonlinear EV battery chargers